Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP

Abstract The Near Surface Concentrations (NSC) of O3, CO, and NO2 are crucial worldwide indicators of air quality. However, current frameworks devised for the estimation of the NSC of O3, CO, and NO2 have defects, such as coarse spatial resolution and large missing coverage. To address this issue, this study aims to estimate the daily (~13:30 local time) full-coverage NSC of O3, CO, and NO2 at a high spatial resolution (0.05° for O3 and NO2; 0.07° for CO) over China by using datasets from S5P-TROPOMI and GEOS-FP. In specific, the light gradient boosting machine is employed to train the estimation models. Validation results show that the NSC of O3, CO, and NO2 are well estimated, with the R2s of 0.91, 0.71, and 0.83 for the sample-based cross validation, respectively. Meanwhile, the proposed framework achieves a satisfactory performance in comparison to the latest related works, as reflected by the estimation accuracy and spatial resolution. As for the mapping, the estimated results show coherent spatial distribution and can accurately grasp the seasonal characteristics of each air pollutant. Finally, the estimated results are utilized to analyze the temporal variations of O3, CO, and NO2 during the COrona VIrus Disease 2019 (COVID-19) lockdown in China, which is an extend application for adopting the proposed framework in air quality monitoring. Results show that the estimated NSC of O3, CO, and NO2 in 2020 present significant variations during different periods of the COVID-19 lockdown in China compared to last year. In addition, the variations in the NSC of O3, CO, and NO2 during the COVID-19 lockdown in China possibly result from restrictions in the anthropogenic activities.